{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machines\n",
    "\n",
    "<hr>\n",
    "\n",
    "### Agenda\n",
    "1. Introduction to Support Vector Machines\n",
    "2. Maximal Margin Classifier\n",
    "3. Soft Margin Classifier\n",
    "2. SVM Algorithm for Classification\n",
    "3. SVM \n",
    "4. SVM for Regression\n",
    "5. Hyper-parameters in SVM\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Introduction to Support Vector Machines\n",
    "* Supervised Learning method used for Classification, Regression & Outlier Detection\n",
    "* SVMs' are effective in high dimension spaces\n",
    "* Uses subset of training data, so memory efficient\n",
    "* Effective in higher dimensional data\n",
    "* Also, in situations where dimension is greater than rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Maximal Margin Classifier\n",
    "* Objective is to find a line( or hyperplane ) which maximizes the separation between data of different classes\n",
    "* The distance between the line and the closest data points is referred to as the margin.\n",
    "* The best or optimal line or hyper-plane is that can separate the two classes is the line that as the largest margin.\n",
    "\n",
    "<img src=\"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQkOQ1Q4Xr1J-J8qbwOZxbV9HmwOs2o2iHo2dLRxVxjq1R0MibU0A\">\n",
    "* Hyperplane is learnt from training data & objective is to maximize the margin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Soft Margin Classifier\n",
    "* In real world, data is always messed up & there is no clear separation of boundries\n",
    "* With Soft Margin Classifier, few data points are allowed to be misclassified\n",
    "\n",
    "<img src=\"https://www.researchgate.net/profile/Catarina_Moreira2/publication/260283043/figure/fig12/AS:297261608259590@1447884098130/Figure-A14-Soft-margin-linear-SVM-classifier.png\" width=\"300px\">\n",
    "\n",
    "* The C parameter trades off correct classification of training examples against maximization of the decision function’s margin. \n",
    "* For larger values of C, a smaller margin will be accepted if the decision function is better at classifying all training points correctly. \n",
    "* A lower C will encourage a larger margin, therefore a simpler decision function, at the cost of training accuracy. \n",
    "* In other words``C`` behaves as a regularization parameter in the SVM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Support Vector Machine\n",
    "* For data linearly separable, SVM works like above mentioned Soft Margin Classifier\n",
    "* Data which is non-linearly separable \n",
    "\n",
    "<img src=\"https://cdn-images-1.medium.com/max/1000/1*C3j5m3E3KviEApHKleILZQ.png\" width=\"300px\">\n",
    "\n",
    "* SVM supports configurable kernel functions which has ability to transform data to higher dimension.\n",
    "* Check if decision boundry exists now to separate the classes in higher dimension.\n",
    "* Transform the decision boundry to original dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_circles\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_circles(n_samples=1000, noise=.05, factor=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19f26c91518>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f269e3cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],c=y,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Doing transformation manually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = np.sum(np.square(X),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z  = np.hstack([X,col.reshape(-1,1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x19f26d35748>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f26d350b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter3D(xs=Z[:,0],ys=Z[:,1],zs=Z[:,2],c=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Now, we can see a possible plane separating the data\n",
    "* SVM does all these thing using libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Kernels\n",
    "* Kernels are integral part of SVM which is responsible of transforming non-linearly separable data to higher dimension such that they are linearly seprable\n",
    "* Different types of kernels are supported by SVM\n",
    "* Every kernel has different way to transforming data into higher dimension\n",
    "* Linear, Polynomial, RBF, Sigmoid & your own custom kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Kernels\n",
    "* Always take simple to complex approach.\n",
    "* If linear separation is possible between classes, linear kernels would work & not otherwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19f27396908>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X243pX1JxRWp1oJ0LX5SAMxyc0am6cvAadZV5lTStYwEgBIEDvZuvCN7TomEo/ybiVPfvG+lHUv4/4Mv8+UjFT7vf1j8Ryj7jbYOD93OLQMlHkMBefRd0jnKpljkUOB9YIiIvp8YuAyYCqOp1wAN4ZZDL8UohL8p/qMaYroh/MtQ84rXdTSwH/95IZL5XARJ9wGux65uElHzQWzyj5XG05RlwRiAlZ3h/BGIL0Oh/wClFIqch/t0K/bb6lDhVMOKBdj+fiUjJB3P+g+aUfRYNzUOb/wkkkfCJSHC/vg06R/YQkzFDgCY3Q+It8I1D/JMAcBNrvU6HvvEQOg7Hyf5BXjUJ8VeBJARme5eABhivVv11cBshMKtoq3vAFuswxuA91KT134fmO72nKjWGBueA+iDevrtiCLf6NpzgrPTtY4vRbZ/FqygRwIHKq5HQof34LrqmiZXotk96veNxQJPosO/glJxZ6NAKynrLGFPEtOk2aL4HiHmtAmiB2AsdEjve+Nb0havVbUS3fSK1sMYO0Eav9/r2z3qfBAYAVRfdeiEkV3v1+9oINEP999H4kkKHV1CW3I0pZk1/om2xjJ2yle8BtOBG/9v2MvoQZLtsqy5E789PfL0Vfwm0jszK65j3h20Is+RuTDFzOz6S0o1kuwpm3U72Wu8W1N3am6jyx91O9nJEFwbIp4tCseRuTDELHUqPfs0jJ7Z9HTyY9AU0UiSCBAfINffgfqmHiTqKQKjwteaFZMndmCImZZd6de2tC0348HqSZ/nVD5+K41S2bRuYCeEP0FY3j/d18MC21r0FJk41lH2W9BjD4J+IlJxWqLAGBKuWMaaIiX8CjHgAbboFYgvBPwUp+TgqpVD3HUi85CX/0ktwSs/N3L7iJxA6Cm2+E0gikQ9C+BS8ZxsHBqfsM2hgH7TpVu8yTfgEpOQsRMKFDq2grM7dmEGsMbqGHc0rGVFei8/vJTPX3Q6Jd8A/A6eLfuyqLrjrQcoRpzwv8biJDV7LYf9eOE4wdZwouFvAqUEk21J1PY9Hk1sAF/H1vI1JrvEMVFbnbkwRi8Y2Ub/uFIYHtxIBNArLW45natl6SLzaOs8NHgaVN2Q8oOQ2PwwNl3sP/eCioaOQih/v8uIcrrsdNn8I3DWpEcENnwVOBTT92VtIGkFLP42UXpJx5q/Rh9H69vHMQyquzIhHEyvR7Zd6D2QB6p+MVPwCCczoNkZVF234BTT9JXUP1kFLL84aTzGwM3djBqHN782lKrid9jlp569yRp4KzcepamsLpfFXvUU9iLabFITgXJzqm3YpHnfjkeBmayflI730MgLl38ApPaddPEvQLed1G49qC7ppHrjbSOuAKcOQmse7Pdt3G6+Bxj+QXhqaGc9Al+uZu91QNWaQqWt8IyOxg5fUs56AtjyY9lIbbyCzh3kMYi96a4T2kJtY2Ulih8ya+mbY8ftdiyf6qPcRpWNrY41D9F9dxqiqsOMmMmv+m2HHdV1uO1hZcjdmkGlofq2HW3RIsMlVZFluASQAyfU9Dyi+vGfz3Q71553GE0yPx13XSZ/05hz+KMW9p2yzxrOlm20HJ0vuxgwyIyqOzDre6RVWiaS/Ds4law9zjYN/es8DCnV7hSBdYI8O8RzQSTyx9HgCs7yE35GUIIHZXR5SJOgtfp2Nv/vr9YORJXdjBplwsIZVzfulJfOdX2fN72VfTXsppR9PJfz2v/4RKL0IcYb1OB7HqYTgUZ18t2PSDiPlX+8QzydS/dQ7xFNyYXo8gQPAPxOvTn+nIPgmphbO6JqUfxuv93qHeIZ9o9ttByNL7sYMQlOm/p2VsdNpSfpJqrAtXsGGwK+QsstSDy0JSCUM+xFO6UfTthXfaGT4PyB8Kjgjwb87UvE9pOx/dzkep/r3UHIRXvIUrzd81Y1I1U0QqAWnBoKHItV/RoIHdIhnFDL8nsx4yi9NnyeCVP8Ryj4Nznhwxnp/kKr/ikj3hX8SPhqp+oP3R6KLeIqFVcsYY8wgYnXuxgxwmljl9VqPPevdzAyfipR/A3FK83qc/658lyuefoIV27YxIlLC5+YeyEdn7VuUtd2mjSV3YwpA3Tp0y1mpdrUuaAKa70ETb0L13/OWeJ9bvYrPPHAf0UQCgI1NO7jy6SdpisX5dO3A6A9j+oZdczemALTpLtBm0mu2YxB/M7WkXX784rlnWhP7Ts2JBNcuXEDCdTvZyhQDS+7GFEJiKelPZKaIeH1h8uSdbdlruOPJJNuiHR/oMcXEkrsxheCfSWZZ3s7vTcvbYaZUVmcdDzg+KkNDu2tisbPkbkwBSMkZ3oLVab+CQfDtBoF98nacLx9yKGF/+q21iN/PJbVzCfiyLMRhioYld2MKQJxKZPgdqdWOHCAEkVOQ6j/ltYrl0AmT+M2J85lSWQXA8EgJXzn4MD5jN1OLnlXLGFMg4p+MVP8RVe3TssRjpkzjmCnT+vw4ZmCx5G5MgdRFo/z2xQU8sPwtwn4/582azfn77IuvY+91VW5fuoQ/vbKYxliMY6dM4/NzD2ZESUmPjtc+sb/fUM+vFzzH06veozoS4VNzajll9z0Klvw1uQ5t/A20PA1OtdeSIDzf/hj1gj2hakwBRBNxTrz1FtY1NhBLel0bI34/R02ZyjUnnpI291uPPcw/3nid5lRJY8BxqI6U8NBHL2RYKJSx7+5s3NHICbfeTENLC8nU73/E7+eTcw7gfw86pJfvrOc0uRHdPB+0gdYOlhKBko/jlH+x3+MZ6KyfuzED2H1vvsHGHTtaEzt49eePvbuC5VvbyhfXNtRz97KlrYkdIO661LVEuX3pkl069h8WL2RHLNaa2Hce+/pFL1Dfkq2lbt/SHTel2vG2a02szbDjBtRt6Pd4ioUld2MK4Pk1q2lOxDPGHRFe2dDWw3zpxo0EnMyqlmgiwbOrV+3SsResWU08ywNMQZ+Pt7ZszrJFH4stADJ/FkigdTk903OW3I0pgIkVlQSzJG1BGF3Wtm7oqLIy3CyXTn0iTKyo2KVjjx9WQbYr2bGkm3bsfuMbD9ki0jg4o/o9nGJhyd2YAvjIXrPwd7hx6hOhOhLh4PETW8dmjRzFxMpK/B1uLAZ8Pi6Yvd8uHfvi/Q8g1KH2PeD4qB07lvHDdu0PRm9I6SdJ79EOEIDgHMQ/vt/jKRaW3I0pgDHl5fzx9A8xftgwwj4/QZ+P2aPH8NczPozTLpGLCLecfiYHjBtP0Ocj7PczqrSM388/jalV2Z8+7c6+o8fwi+NOoDocIeL3jn3k5Mlce9Kp+Xp7PSLB2VDxU5AqIAIEIXQEUnlNQeIpFlYtY0wBqSprGxoI+n3UlHTd6ndrcxM7YnHGDxuWlxLBpOvyfkM9FaEwFeHCtyJQTULyfXAqEKf/P0EMFnnr5y4iNwHzgY2quneW788D7gXeTQ3drarf71m4xgxOq+q2c9NLi3hzy2ZmjxrNhfvOYXRZeU7bvl9Xx+m338qWZq+B154jRnD/ORfw0DvLufP110ioy4f2mMnJ02fw4tr3+curL7O9JcqJ06Zz5sy9My6tADy/ZhXf++/jrG2oZ0pVFT+Ydyx7jwigTX+G2ELwT0VKPwbOKKTp74xPPAE6EnUu8M6gC0jEB/6J3U80Oen2zF1EjgAagVu6SO5fUdX5PTmwnbmbwe6V9es47547iCWTJFyXgOMj7Pdx94fPZVr18C633dzUxNwbfpf1eyX+AE2pSpoSf4Bx5eWsaahvLYeM+P1MrarmzrPOSUvwdyxdwtcf/U/avsaWNPDoyf8kIC1ADPABfnAqwK3H60wpQAiGXY5Tcsau/TBMv8lbnbuqPglszUtUxhSRbz3+CE3xeGtf9LibpDEW44dPPdHttmfefmun32tqVyLZlIjz9rataXXuzYkEK7Zt5d43l6Vtd/kTj2Xs68uzXsDRRrzEDl4teQu4G2lrOaze1w0/QDVLG2IzKOXrhurBIvKKiDwoInvlaZ/GDFixZJI3Nm/KGFfg+TVrut1+VX19r47fnEjw0Dtvt75ujMWIJhMZ8w4fvQafk+t9NQfiy7qfZgaFfCT3xcAkVZ0N/Ab4R2cTReRiEVkoIgs3bcr8xTBmsPA7DgEn+69PWTDQ7fa+rJXmuXNEGNHuBmzHtr47NcS7j6WVJsEZ1qu4zMDR6+SuqvWq2pj6+gEgICIjOpl7varWqmptTU1Nbw9tTME4Inxoz5mEOvRE9xqA7dvt9ufu3bOe7R3/FAR9Ps6d1XYD1O84TKuqytju5rdmEU12TPB+vGvv7Tngn4jkcaEQU1i9Tu4iMlpSdVkiMje1z+xrexlTRL59+FEcNH4CIZ+f8mCQkM/HcVOn8bkDDux22+8dfSyjs5Q+HjJuApWhMGXBIGXBIOXBID866jjGlg+jNBCgPBgk7Pfz7cPnMXvU6LRt/37G2VSFI2ljj244GH/kNCAIUg6EIbAflF4ChEDKQErANwWpur4XPw0z0ORSLfNXYB4wAtgAXA4EAFT1OhH5PPAZIAE0A5eq6rPdHdiqZUyxWLl9G+9t385uw4czrrxnlzVe37iByx57mGGhENfNP52SQIB4MslL69eRdF32HzuOoM+HqvLKhvU0xFrYb/RYyoLBTvf5zKr3eGn9Wg6ZMIk5Y8YCoMkNXp8W39jWs3N16yC+BJwq8M+09rqDRK7VMvYQkxnylm3exP1vvUHSdTlp+gz26XBGnA+xRIJrFi7g0RXvMDxSwlcPOYxZnRwnWzwvrFnDrxY8S0OshZOnz+DiObVsiTZzz7LXWd/YwAHjxnPc1N0yWhoUmrqNaPM/IbEcCewFkRMRiXS/oemUJXdjcnDti89z7YsLvNa7qoT8fs7fZz++cdgReTtGYyzGITf9nsZYLG38a4ccziUdlrvLFs+0qmpe27QxbV5lKETMdUm6Li3JJCWBAFMqq7j9zLOJBHpwE7UPaWIVuuUs0Cjeh/oScIYhw+9EfCMLHd6gZf3cjenGe9u3c80LzxNNJHBVcfFKDG959SWWdUimvfHNRx/KSOwAP3v2KaLt6tdX1WWPp2NiB9je0kJTPE5Lqh98UzzO8q1buOmlRXmLu7e0/tugdXiJHaAJ3E1ow5WFDGvIsORuhqzHVq4g2+fWWDLJwyuW5+04j698N+u4Ave/9Wbr60ffXdGr47Qkk/zjzYFRp66agNiLQMe+8UloebQQIQ05ltzNkOV3nLQOjDv5RAj4Mnut7ypfFzcqw/624/gdp9c3NfMZd+8IWXu0A7Z0c/+w5G6GrA9M241s95wcEU7abUbejnPqjD2zjnvH2b319QnTptObW2ARv59zelg/31dEfBA6msxEHoDwKdk2MXlmyd0MWSNLy7jy2A8Q8vko8QeI+P2EfD6+ffg8JlVW5u04lx95NBOyLILxqw+chNOuuqWmtJQrjz2ekM9HpF087f8A7DS9qprhkRJKA8HUfD+HTZw8YJI7gFR8H3wTQErxaupLwL87Uv6VQoc2JFi1jBnytjQ18ci77+CqcsyUqYws7Zul5h56523++eYbjCot4wsHHkRlOHtJYLZ4Nu1o5NcvPM/2aJSz95rFoRMnEUsmeezdFWzc0cicMWPZe+TAW5JO1YXYM5B4F/wzIDjX6ul7KW/93I0Z6FSVVzes57VNGxlfPozDJk7C14N67+ElJXxkr1lpY6vr6vju449Q1xLl4/vuz/wZe9AUi/HbhQtYU1/P8dOmc9L03Um4Lje9tJClmzZRO3Ys5+3ttQS4a9lSnl69iunV1Vw85wCCfj+7VVVzyISJVEdKiPi9csW1DfU89d5KwoEAR0+eSnkohCNCwHFIquKI9z5qSsv4wVHHpsUY9Pk4YbfpvfnR9TkRB0KHe/9Mv7IzdzOotSQSXHTf3byyfj2qis9xqI5EuP3Msxm1i4s9X/HUE9zYoaSwIhikPhZLq64ZHimhLtpMot3vUNjnI+Dz0dCu9NEnwlGTp/L0qvdAwCcOAZ/Dabvvyd+WvoojgiOCKnxs3/246aXF+BwBhaS6XH7k0Zw9gC63mMKyh5jMkHDVc8/wh8ULaWnX7tYnwoHjJ/CXD57V4/1ta25m/z/8Np8hdkogaylmRyGfn4c++jEmVuTvPoAZvOwhJjMqdNqCAAAaCklEQVQk3PH6a2mJHSCpygvvr8n64FB3rnzmv/kKrVu5nla56vKvdvXwxuTCkrsZ1OJuMuu44C0A3VPRRPb9FVLS1dYnUY3JlSV3M6idMG33rM2ydqseTkU43OP9XXrQIfkIK6+Cfq+VsDE9YcndDGpfPvhQxpSVU5JqlhX2+ykPhvj58Sfu0v4mVVZx2PiJGePZVk7KthKTQNanXqdUVrXGGHR8hP1+Dho3nhJ/AMG7TxD2+zl68lTCfj8O3k3WiN/PuXvvw14DsMzRDGx2Q9UMetFEnH+9/RYvrVvHlKoqPrTHTKoivWsre/frS/n5c08TTcQ5Zuo0rjj6eJZv3cyVTz/FhsZGDps4iS8ffChbo8386KkneHvrVmaPGsU3DpuH3xF+8sxTLFq7lsmVlXzzsCOZUFHBY+++w1Or3mNkaSln7LkXY8rKeX7Nav6zYjkRf4AP7jGT6cOH89rGDfzzzTdIqsvJ02ewX6onuzFg1TLG5Czpury8YR1JV9lv9BgCqcUxXtu0kcaWFmaPHtN61p3Nmvo6Vm7fzm7V1YwuK+90Xn1LC0s2rmd4pIQZw0f06GGensRjips9xGRMDhaufZ9L7r+XlmQSEe/yyGWHz+PaF59nc1MTPhESrsv/HXk0Z3V40CmaiPOFB//F06tWEvT5aEkmOXn6DH5y7Acy7gNcv+hFfvn8swR9DglXmVhRwR9P+1CXfwx2enf7Ni66965u4zGmPbvmboas+pYWLrr3LrZGm9kRj9EYi1HX0sLXH3mI1XV1NMXjNMRiNCcSXP7fx3ht44a07a948r88vWolLckkDbEYsWSSB5e/xe9eXJA276n3VnL1gmdpSSZS+/N6r3/yvnu6jdFV5fy778gaz5IO8RjTniV3M2Q99M7bnXZh7DgcSyb5y6svt752Vblz2WsZJYrRRIJb2s0DuOnlRTQnMmvxV2zfxoptW7uMcdG696lriWaN59YOxzGmPUvuZsiqi0Y7rZPvyFVlU9OO1tcJ1yXeSR19x4entjY3Z53ndxy2R6PdxihZKnU6xmNMR5bczZB18PgJOTcYi/j9HD91t9bXQZ+PGcNHZMwTYO648Wljx0yZRijLIhpJV5lZU9PlceeMGZv1D1DHeIzpyJK7GbL2GjmKE6ZNT6s8ifi9haYj/rZag7Dfz+TKKk7fY2ba9j886lgifn/rSksBx6E0GOTbh89Lm3fhvvtRU1pKyOftU/CS83eOmEfY33XVS3WkhM/PPSgjnkmVVZy2R/ZFQIwBK4U0Q5yryr+Xv8Xfly4h6SpnztyLU3bfg+fWrObPr77M9mgzJ02fwYdn7k0kS/nhu9u3cePihbyxZTP7jhrNx/fbn7HlwzLm1be08Nclr/DoyhWMLC3lon3nsP+YcTnH+fSq93KKxxQ/q3M3/c5VZV1jA+XBEMNCoUKHk5WqsmFHIyGfP+1Bp81NTagqNaWlBYzOmO5ZnbvpV/9e/hbfefxRdsRjuKocPXkqPz3uBMqCwUKH1mrxurVc+p8H2NDYiKuw3+gxXHrIofzwv4/z1tYtAEyuqORXJ5zMHiO6vhZuzEBnZ+6m115Zv45z7r6daLtyv6DPx0HjJvCn088oYGRt1jc2cOyf/0hTPN465hNB8T5xtDcsFOKpCz9F+QD99GGGNuvnbvrN7xe9SEuHOu5YMsmC91fzfkN9gaJK9/elS0h0KF1MqmYkdoB4Msm/3rb+6WZws+Ruem1VfV3WhSeCPh8bGhv7PZ5sVm7fTizHnujNicSA+aNkzK6y5G567cCx47O2v40lk+xWXV2AiDIdOG5866LU3SkNBJg9anQfR2RM37LkbnrtU/vXUhIIpPUxj/j9fHJOLcNCPV8woy+cNmNPhpdE0v4Ihf1+KsPh1vpz8D5tTKqo5KjJUwsRpjF5YzdUTV6sqa/jquee4dnVq6iORLh4/wM4bcaePWpr29e2NTdzzQvP8+A7bxH2+Tl31mzO2XsfbnxpEXctW0pSXU6fsSefqT2Q0gFU5WNMe1bnbowxRShvde4ichMwH9ioqntn+b4AVwMnAU3Ahaq6uOchm6Fqe7SZ8+6+g2WbNwFeKeJVx5/I0VMy1w39xiMPceeypbiq+B2Hj+87h28cdmTGvFc3rOd7/32MVzespywY5ILZ+/GFuQdnXW+1o5ZEgp888xR3vv4azYk4c8eN5/Ijj+aRFe9w40uLqGuJsueIGi4/8mhqx+b+lKkx/anbM3cROQJoBG7pJLmfBHwBL7kfCFytqgd2d2A7czc77X/9tWzL0h3x/nM+ysyatrVD//ehB7j3zWUZ8z5bO5evHHJ46+sV27Zy6t/+klbTHvb7mT99Bj897oRu47no3rt4fs3q1na+gtfB0ec4abX8Yb+fO84829Y3Nf0qb3Xuqvok0FXT6dPwEr+q6vNApYiMyT1UM5Q99u47WRM7wP898Vjr167rcl+WxA7wh8XpJwnZ6u6jiQT3vfUGm5uauoznna1bWPD+mrQ+7QrEXTctsYN3hv+bF57vcn/GFEo+qmXGAavbvV6TGjOmWy+ufb/T763Ytq3168ZYLGstPZDRV33ppo0ks3wiDfl8rNy+LWO8veXbtuZ06Qa8pP/Glk05zTWmv+UjuWcrh8j6eygiF4vIQhFZuGmT/VIYOKCLa9ZTKqtavy4LBrP+Hw3IqLGfOaKmtQ1ve7FkkkmVlV3GM62qOuNJ1s4IZO3pbsxAkI/kvgaY0O71eGBttomqer2q1qpqbU03ixSYoeHoKdOo7KQW/vJ5R7d+7TgOJ0+fkXXeJ/bbP+31p/c/gGCHxTHCfj8nTZ9BTUnXXR93qx5O7ZhxGYtrBByHsC+9/iDs9/OFuQd3uT9jCiUfyf0+4ALxHATUqeq6POzXDBGPnH8Ru7c7Ay4PBrnu5FPZu8ONyl+fOJ/TZ+zZ+rCUT4SP7zuHrx16RNq8adXDufVDH2bWyFEI3ln/hbPncOUxx+cUz/WnnMaH95pFxO89mDV37Hju+ch5fKZ2LhWpZmJ7jqjhplM/lBGjMQNFLtUyfwXmASOADcDlQABAVa9LlUJeA5yAVwp5kap2WwZj1TImG9d1cXK45p3rPFXt1YNU2bbv7T6N6Y281bmr6jndfF+Bz/UgNmM6lUvC7sm83ibhbNtbYjeDgfWWMcaYImTJ3RhjipAld2OMKUKW3I0xpghZcjfGmCJkyd0YY4qQJXdjjClCltyNMaYIWXI3xpgiZMndGGOKkCV3Y4wpQpbcjTGmCFlyN8aYImTJ3RhjipAld2OMKUKW3I0xpghZcjfGmCJkyd0YY4qQJXdjjClCltyNMaYIWXI3xpgiZMndGGOKkCV3Y4wpQv5CBzCYvLdsDUueXEZFzTAOPHkOwVCg0CEZY0xWltxz4Louv/jE7/jv7c8C4Ph9BEJ+fv7o5UyZNanA0RljTCa7LJODJ/72DE/e+RwtzTFammM0NzRTv7mB757+U1S10OEZY0wGS+45uP/3DxPd0ZIxvn1jHStfW1WAiIwxpmuW3HMQa4lnHRfHIdaS6OdojDGme5bcc3DMuYcRKglmjAeCfnbbd3L/B2SMMd2w5J6Dky8+jqn7TCZcFgYgEPITKgnxzVu/iM/vK3B0xhiTyaplchAMB/nlk9/n+fsXsejhVxg+porjLzyKmvHDCx2aMcZkZck9Rz6/j0NPn8uhp89NG49FYyx7/m1CJUF2r52G49iHIWNM4eWU3EXkBOBqwAfcoKpXdvj+hcDPgPdTQ9eo6g15jHNAeuLvz3DVp65DHEFdpayylCv+9U2rfTfGFFy3p5ki4gOuBU4EZgLniMjMLFP/rqr7pv4VfWJf9cb7/Pzjv6W5MUpTfTPNjVE2rdnCV4/9Pom4VdAYYworl2sIc4HlqrpCVWPA34DT+jasge/BGx8lEU9mjMejcRY9/GoBIjLGmDa5JPdxwOp2r9ekxjo6Q0ReFZE7RWRCth2JyMUislBEFm7atGkXwh04tm+oI5nITO6qSsPWxgJEZIwxbXJJ7pJlrOMz9/8EJqvqPsAjwM3ZdqSq16tqrarW1tTU9CzSAebAk+cQLg1njCfiSfY5MttVK2OM6T+5JPc1QPsz8fHA2vYTVHWLqu58Pv8PwP75CW/gOuxDBzJl1kRCJaHWsXBpiA/+z4mMnDCigJEZY0xu1TIvAtNFZApeNczZwLntJ4jIGFVdl3p5KrAsr1EOQP6An58//n/8509P8MTfnyFSFmb+Jccz98T9Ch2aMcZ0n9xVNSEinwcewiuFvElVl4rI94GFqnof8D8iciqQALYCF/ZhzL3S1NBM47ZGho+rxufr/OnSxrpGXnrkNXY/YBqjJnqXkFzXZfOaLZRWlFBaUUowFODki4/loPlzCIaDDBte3uWx67c20NIUY8S4akSyXe0yxpj8kEK1rK2trdWFCxf22/GiTS388uLreOquBTg+IRQJ8dmrL+KYcw/PmHvxvl/m3Vfbuj2WV5fxuV9fxHWX3kJzQzOu63LQKbWcfPFxXH3J9WxZuxXXVfY8aHcuu+2LjBhbnba/7Zvq+PF5v2bJk68jjlA5soKv/vFz7HvU3n3+vo0xxUVEFqlqbbfzhkpy//6Hf8GC+xcRi7Z1eAyVBLni/suYPW+v1rHLTrqCF//9crf78wf9JBNJ1G37+Tk+h9FTRvLHN65ufVJVVblkzld5b+matOqaUEmI37/8M8btNiYfb88YM0TkmtyHxLPy2zfV8fw/0xM7QEtTjL/++O60sRcf6j6xAyRiibTEDuAmXbZt2M6SJ9tuOby1aAVrl6/PKJtMxBPc99uHevI2jDEmZ0MiuW9dt51AMPvthfUrN6YP9PaDjMLG1ZtbX25ctRnHl/ljTsaTvP/2uoxxY4zJhyGR3MfuNhrXdTPGHZ/DrMP2TBvz+Xv3I3GTLrvXTmt9vfv+U0nEMtsRhCLBtMtBxhiTT0MiuYdLQnz0u2el1aQ7jhAuDXHut85Im3v+5R/Oug/HJzhOW4VLqCRIaUUJ/nafCEIlQeaePIdJe45vHRs1qYajzjks7di+gI+yqlJO+uQxvX5vxhiTzZBp+fuRr57G6Mkj+duV97B1/XZmHb4nF/7gbMZMHZU277xvnYHP73Dz5beTiCUQRzj2/CO44PIP86fv/p2XHl1CRU05H/7KaRxwwr785Qd38vQ9CwhFQpxyyXGc/oWTMo596R8uYfr+U7n3mgdpaohyyKm1nP/dsyitKO2vt2+MGWKGTLWMMcYUg1yrZYbMmXsikeBbJ/2Ylx59FVUIRoJ8+ucX8NhtT7H0mTdb5x1yei0nf+p4rvvyzax5ay1Voyo579tncMolx2c8eLT2nfVc8/kbWfzoEgIhP8d+9Agu/tn5RMoi/f32jDEmzZA5c//0fl9lxSsrc5rrOE7aDdhwSYjz/+/DfPgrp7aO1W9t4MIZ/0Pjth2tJZGBUIDda6fyyyd/YE+gGmP6hNW5t7Nx1aacEzuQUVkTbWrhtivuSqtV//dNjxNriqXVusdb4rzz8kreWvhOr2M2xpjeGBLJ/Y0Xlvd6H7FonIZtbX3al7/0Li3NsYx5IsKqZe9njBtjTH8aEsl9j7m79XofgZCf8qqy1te77TuZUCSYMU9VmbDH2F4fzxhjemNIJPeRE2uYvHfWxaGy2tkXZqdwaYhzvvlBfP62LpInfPxogpFg2rX1QMjPlFkTmXFA7/+YGGNMbwyJ5A5w7cKfeE+EpnJxMBzgkqs+xh4HpifiA+fP4Xv/+Brjdh8DAlWjKvjEj87lI187PW3esOHl/Pq5H7HfMbNwfA7BSJBjzz+SKx/6jt1MNcYU3JCplmnPdd2Ms/NkMpnR311Vc0rUuc4zxpjeKso69/qtDdx2xd08ffcCQpEg8z9zPKd+9gMZSTmRSPCzC6/lqbueJ5lwmbTXBL711y/x049d01rJ4vgcPnnledx51f1sXbetddtJM8dTMXIYrz7xeuvY8HHVnPa5E7jpW7e1NharmTCc37zwY+742X08fVfX8biuywN/eJR/XPMAzQ1RDjn1AM799hlUjazoo5+UMWaoGzRn7tGmFi7e58tsXrOFeKoRV6gkxCGn1nLZbV9Km3vRnl9kzZtrs+0m7wJBf1o8B5+yP9/66/+mzfnlxdfx2G1PE23ylpn1B3xUjqrghtd+Semwkn6J0xhTHIquzv2xW59i24btrYkUoKWphWfufZHVb7aVHr7+3Jv9ltiBjHievW8hq95oi2fjqk08/JcnWxM7QCKepGFrIw/e8Gi/xWmMGVoGTXJ/+YmlRHe0ZIz7fE5aHfsz977Yn2Fl8Pkc3nyxLZ63Fq3I2ku+pSnGy4+/1p+hGWOGkEGT3MdOG5V9wQ2BkRNGtL6ctOe4fowqC4Ga8cNbX9aMH47rZl768vl9jJ02uj8jM8YMIYMmuZ/0qWPxBdJvVDo+h6qRFcw6om3BjWPPP5JAKNDf4aXFs8+RM1vHdq+dxpgpI9Nq5MG7Vn/q507o7xCNMUPEoEnuIyeM4EcPfItRk2oIRoIEQn72PGg6P3/8e2lljY7jcO2LV1JWWdpuTPjQl05GfOnliiOn1uR8/KoxmZUtl/zqwm7jERF+8p/vMOvwPQmE/IQiQUaMq+Z7//ga46fb4tjGmL4xaKpldlJVNq7aTDAS7LaUcPWb77N9Uz17HTKjNeG+9swy3nzxHT5w4TzKKr12As/cu4AHb3iMj3z9NGYd5p11L395BXf+4n7mnX0IB53s3Ziuq9vB3T+/jz0Oms7BqbGexFO3uZ7mxiijJtVYXbwxZpfkWi0z6JJ7R6rK03cv4N9/fBx1XY67YB5HnHVQRq15Z156bAm//d8/sXHVZibMGMv/XPMJAuEgV19yPe8uXc2IsVV8+hcXMPeEOb2O1RhjemvIJPeffOw3PH33gtZKmnBpiNoP7Mt37/hyt2fHD970GFd98nc5HeeSqz7GGV+a3+t4jTGmN4quzj2btxev4Km7FqSVSEZ3tLDwoZdZ+swb3W7/m8/fkPOx/vC1v2T0eTfGmIFqUCf3lx5dQjKeyBiPNrWw6JFXu9x2R30T8Wg852MlE0nWvbOhxzEaY0whDOrkXlZVhj9L7XswFEjrvZ5NINzzcsny4V3v0xhjBopBndyPOPOgrOPiOBx19qFdbhsMBhi/e+6LatRMGM6w6vIexWeMMYUyqJN7WWUpP7z/m5RXl1EyLELJsAilFSVcftdXqBpV2e32v3z6B1SMSE/YIyeNYOz09CdHSytLuPqZK/IauzHG9KVBXy0DkIgneP25t1BXmXnI7gSCPbvksvjRV1n23FvMOW4f9jxwdwDeWryCF//9EtPnTLEySGPMgJHXUkgROQG4GvABN6jqlR2+HwJuAfYHtgAfUdWVXe2zkIt1GGPMYJW3UkgR8QHXAicCM4FzRGRmh2mfALap6m7AL4Gf9DxkY4wx+ZLLNfe5wHJVXaGqMeBvwGkd5pwG3Jz6+k7gGLHn640xpmBySe7jgNXtXq9JjWWdo6oJoA4YjjHGmILIJblnOwPveKE+lzmIyMUislBEFm7atCmX+IwxxuyCXJL7GmBCu9fjgY7r2LXOERE/UAFs7bgjVb1eVWtVtbamJvd2u8YYY3oml+T+IjBdRKaISBA4G7ivw5z7gI+lvj4TeEwLVWNpjDEm51LIk4Bf4ZVC3qSqV4jI94GFqnqfiISBPwP74Z2xn62qK7rZ5ybgvV7EPgLY3IvtB5Jiei9QXO+nmN4LFNf7GarvZZKqdnvpo2APMfWWiCzMpdZzMCim9wLF9X6K6b1Acb0fey9dG9TtB4wxxmRnyd0YY4rQYE7u1xc6gDwqpvcCxfV+ium9QHG9H3svXRi019yNMcZ0bjCfuRtjjOnEoEvuInKTiGwUkdcKHUtvicgEEXlcRJaJyFIR+WKhY9pVIhIWkRdE5JXUe/leoWPqLRHxichLInJ/oWPpLRFZKSJLRORlERn07VhFpFJE7hSRN1K/PwcXOqZdISIzUv+b7PxXLyJfysu+B9tlGRE5AmgEblHVvQsdT2+IyBhgjKouFpFyYBFwuqq+XuDQeizVKK5UVRtFJAA8DXxRVZ8vcGi7TEQuBWqBYao6v9Dx9IaIrARqVbUo6sJF5GbgKVW9IfVwZYmqbi90XL2R6sD7PnCgqvbmGSBgEJ65q+qTZGltMBip6jpVXZz6ugFYRmZTtkFBPY2pl4HUv8F15tCOiIwHTgZuKHQsJp2IDAOOAG4EUNXYYE/sKccA7+QjscMgTO7FSkQm4z3hu6Cwkey61GWMl4GNwMOqOmjfC94T2V8D3EIHkicK/EdEFonIxYUOppemApuAP6Yum90gIqWFDioPzgb+mq+dWXIfAESkDLgL+JKq1hc6nl2lqklV3RevudxcERmUl81EZD6wUVUXFTqWPDpUVefgLbrzudTlzcHKD8wBfqeq+wE7gG8UNqTeSV1aOhW4I1/7tOReYKnr03cBt6rq3YWOJx9SH5GfAE4ocCi76lDg1NR16r8BR4vIXwobUu+o6trUfzcC9+AtwjNYrQHWtPtkeCdesh/MTgQWq+qGfO3QknsBpW5C3ggsU9WrCh1Pb4hIjYhUpr6OAMcCbxQ2ql2jqt9U1fGqOhnvo/JjqvrRAoe1y0SkNHXDntTli+OBQVttpqrrgdUiMiM1dAww6IoQOjiHPF6SAe/jzaAiIn8F5gEjRGQNcLmq3ljYqHbZocD5wJLUtWqAy1T1gQLGtKvGADen7vg7wO2qOuhLCIvEKOCe1MqXfuA2Vf13YUPqtS8At6YuZ6wALipwPLtMREqA44BP53W/g60U0hhjTPfssowxxhQhS+7GGFOELLkbY0wRsuRujDFFyJK7McYUIUvuxhhThCy5G2NMEbLkbowxRej/Ab2j3EJsGkmCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f27344c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(iris_data.data[:,2], iris_data.data[:,3],c=iris_data.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(iris_data.data[:,[2,3]], iris_data.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris_data.data[:,[2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = iris_data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = .02\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19f273ff898>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f27374780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = Z.reshape(xx.shape)\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "plt.scatter(X[:,0],X[:,1],c=y,s=10)\n",
    "plt.scatter(svc.support_vectors_[:,0], svc.support_vectors_[:,1], c='r',s=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We can see linear separators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.9, 0.4],\n",
       "       [4.7, 1.4],\n",
       "       [4.9, 1.5],\n",
       "       [4.6, 1.5],\n",
       "       [4.7, 1.6],\n",
       "       [4.7, 1.4],\n",
       "       [4.8, 1.8],\n",
       "       [4.9, 1.5],\n",
       "       [4.8, 1.4],\n",
       "       [5. , 1.7],\n",
       "       [5.1, 1.6],\n",
       "       [4.5, 1.6],\n",
       "       [4.7, 1.5],\n",
       "       [3. , 1.1],\n",
       "       [4.5, 1.7],\n",
       "       [5. , 1.5],\n",
       "       [4.9, 2. ],\n",
       "       [4.9, 1.8],\n",
       "       [4.8, 1.8],\n",
       "       [4.9, 1.8],\n",
       "       [5.1, 1.5],\n",
       "       [5.6, 1.4],\n",
       "       [4.8, 1.8],\n",
       "       [5.1, 1.9],\n",
       "       [5. , 1.9],\n",
       "       [5.1, 1.8]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.support_vectors_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Polynomial Kernel\n",
    "* Transforming data to higher order polynomial.\n",
    "\n",
    "<img src=\"https://github.com/awantik/machine-learning-slides/blob/master/svm24.PNG?raw=true\" width=\"300px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y = make_moons(n_samples=1000, noise=.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19f2762cc88>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f27396dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], c=Y, s=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='poly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\awant\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='poly', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = .02\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19f2769b518>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f273bb3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = Z.reshape(xx.shape)\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "plt.scatter(X[:,0],X[:,1],c=Y,s=10)\n",
    "plt.scatter(svc.support_vectors_[:,0], svc.support_vectors_[:,1], c='r',s=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\awant\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19f277078d0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f276223c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc = SVC(kernel='poly', C=10)\n",
    "svc.fit(X,Y)\n",
    "Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "plt.scatter(X[:,0],X[:,1],c=Y,s=10)\n",
    "plt.scatter(svc.support_vectors_[:,0], svc.support_vectors_[:,1], c='r',s=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RBF Kernel\n",
    "* As we see in the above example, linear or polynomial decision boundery cannot separate classes\n",
    "* RBF is a kernel trick to transform data non-linearly & then predict the separator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\awant\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19f277717b8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f276deba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc = SVC(kernel='rbf')\n",
    "svc.fit(X,Y)\n",
    "Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "plt.scatter(X[:,0],X[:,1],c=Y,s=10)\n",
    "plt.scatter(svc.support_vectors_[:,0], svc.support_vectors_[:,1], c='r',s=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Hyper-parameters : C\n",
    "* The C parameter trades off correct classification of training examples against maximization of the decision function’s margin.\n",
    "* For larger values of C, a smaller margin will be accepted if the decision function is better at classifying all training points correctly. \n",
    "* A lower C will encourage a larger margin, therefore a simpler decision function, at the cost of training accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y = make_moons(n_samples=100, noise=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b51af404e0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b51aec3a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], c=Y, s=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = .02\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\awant\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b51b5dffd0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b51b2dc1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc = SVC(kernel='rbf', C=0.1)\n",
    "svc.fit(X,Y)\n",
    "Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "plt.scatter(X[:,0],X[:,1],c=Y,s=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\awant\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b51b8fec88>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b51b2dc208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc = SVC(kernel='rbf', C=100000)\n",
    "svc.fit(X,Y)\n",
    "Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "plt.scatter(X[:,0],X[:,1],c=Y,s=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Understanding Hyper-parameter : Gamma\n",
    "* Gamma is a parameter to handle non-linear classification\n",
    "* By increasing value of gamma, we are increasing importance of each point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19f2d1d0438>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f28c8b748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc = SVC(kernel='rbf', C=100000, gamma=1)\n",
    "svc.fit(X,Y)\n",
    "Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "plt.scatter(X[:,0],X[:,1],c=Y,s=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Using Grid-Search, we need to find right combination of kernel, C & gamma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM for Regression\n",
    "* Target data is continues in nature\n",
    "* The relationship between feature data & target can be linear as well as non-linear.\n",
    "* Based on that the kernel can be chosen - linear, poly or rbf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.sort(5 * np.random.rand(40, 1), axis=0)\n",
    "y = np.sin(X).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19f2d5d6438>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f2d3dc4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y,s=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "svrs = [SVR(kernel='linear'), SVR(kernel='poly'), SVR(kernel='rbf')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\awant\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\awant\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "for model in svrs:\n",
    "    model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19f2f03cd30>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wu8VsQkaEMKTWEHS0dPjr/F9UnV8VL18vwh6FZct5MzuaJyEhme++O4Ct7Tw2bLiEsbEu06Y15ty5Qfku8GfEzMyA9u2rsGCBJ8HBIwgJGcGvv35Mx45VMDMzIDT0Eb/+epKOHddibj6DkSO3k5iY8vYDS3lKeHg4//77L6CmRW7QoMFr0yi/rE6dOty4cYO//vqL7t3fuBbVGzVs2JCVK1cCEBQURHh4ODY2Nul+fuHCBc6dU4dZvy61tCbI4P8GpQuXZv7H87k6/CqfOn4KwNIzS7GeZ81nmz/jRtSNLD1fZoYmbt9+FTu7BUyY4E9cXDJdulTj8uVhjBvXoMCmUrCyKsJnn9Vi/fou3L//JUeOfMqUKR40bFgORYG5c4/h4bGM//578tZjvU+zm6QZVapUYdmyZTg4OPDw4UMGDx6cLo2yvb09WlpaDBo0KMPvd+nShfr161OkyPuPgBsyZAgpKSnY29vTtWtXli5dir6+PoMHDyY6OhoHBwdmzJiBs7MzkD61tIODA3Xr1uXy5cvvff4PUSAmeWWV4IfB+Oz3YeX5laSKVPS09RjoNJCvXb/mI5OPsuQcr5vBm5iYwujRO5k//zgAVapY8PPPLWnc2CpLzptfHTt2i44d13Lz5mNKlDBm7drONGxYLsN95YzgzNP0JK9r167h6emZlgf/fXh6ejJq1CgaN26chSXLWXKSVw6pVLQSy9sv5+KQi3St1pXElETmHZ+H1Vwrxuwcw72Ye28/yFs0rVoCn7Z26YLOzZuPcXNbyvz5x9HT02bGjCacPTtIBv5McHYuzcmTA/HwKM/duzE0arSM2bOPZNhW+66T6KS8KTIyEmtrawwNDfN04P9QMvi/B1sLW1Z3Ws25Qedob9ue+OR4Zh6ZidUcK77a/RUPYh9k2bn8/cOoWXMRR47cpGzZwgQEePHll/XlgirvoHhxY3bt6s2XX9YjJUUwatROevbcSExMYrr95IzgvKN8+fLvfddvZmZGUFAQ69aty+JS5S2y2ScLnLp9im/3fcuWIHW5NRM9E0bVHcUol1GYGZi91zGFEPz442HGj99DaqqgSRMrVq3qKPPlf6D16wPx8vIlOjoRO7vibNzYhcqVzdO2yyygmaPpZh9JJRO75RJHbx7Fe583u0J2AWBmYMYXLl8wos4ITPRNgMwFl8ePE/Dy8k0buvj11w3w8fFAW1s+qGWFS5ciaN9+DVeuPMDUVJ8//2xP69Y2r91fVgivksE/d5DBP5cJCA9gov9E9l3bB4C5oTnj6o/DxqQj49YFvbFDMTAwgg4d1MBUuLA+y5e3o21bWw1cRf72+HEC/fr9w99/qyMtJkxwZdIk91cqWNkJnDEZ/HMH2eGbyzSwbIB/X3/29NlDvbL1eBD3gLG7x9JjszN3UzciSMywQ3H79qvUrfs7V648wM6uOCdODJCBP5sULqzPhg1dmDatMVpaClOnHsTTcxUPH8al2092Akv5lQz+2ahRhUYEeAWwved2apWqRUzyAx7p/cYtg/7E622njtXzJRJ//vkonp6rePIkkS5dqnHkyKfp2qKlrKcoCuPGNWDnzl6YmxuyY0cwtWot4vTp22n7yE7gvKNQoYwXB7p8+TI1atTA0dGRkJCQbDt/bkzb/CYy+GczRVFoUakFx/ofw7ebL1am1UhRHnJXez5D9riy8NhvDBm6hREjdpCaKvD2bsiqVR0xNtbTdNELjCZNrDh16jNq1SpFWFgk9eotZtOmK8DztNB9XMrJJp9cSghBamrqa7f/888/tG3bltOnT1OxYsUcLFnuJoN/DlEUhTY2bbg68hzrO6+nWrFqhN+9y6AeB1jwy0l0dGH58rZMnuyBllbeWFksP7G0NOXgQS/69atBfHwy7duvYeFC9Q4to7kXkmZdu3aNKlWqMGTIEJycnLhxQ51tP2bMGJycnGjcuDERERFs27aN2bNn8/vvv+Ph4fHKcQoVKsS4ceOoWbMmTZo04dixY7i7u2NlZcWmTZsAiI+Px8vLC3t7exwdHfH39wcgLi6Obt264eDgQNeuXYmLe95kuGvXLlxcXHBycqJz585ER0e/cm5Ny3/5AFJS4OlfGgDPkiu9+GdGLy2t569nn7W1X33p6KR/r6v7/E89vecv7YzH4WspWnSs2pHq+m64N/+NWyGJYBRDcrfVfBe1Cr0Lk+lcrTNaiqyXc5qBgQ6LF7ehXDlTJk/ez6BBW7l16wmTJ7vnmaU+NUFRJmfLcYX49o3br1y5wpIlS/jll18AiImJwcnJiZ9++gkfHx8mT57MvHnzGDRoEIUKFeKLL7545RgxMTG4u7szffp02rdvz4QJE/Dz8yMwMJC+ffvSpk0b5s+fD8D58+e5fPkyzZo1IygoiAULFmBkZMS5c+c4d+4cTk5OANy/f5+pU6eye/dujI2NmT59OjNnzsTb2zuLf0MfJv8F/6Qk6NBB06UgVUsLoa+PtoEBPHvp64OBAYeTStIuyImIJD2qFo7nZ+cz3Lgaxe0rN7i4oRvh5p/TuHp7HK3dUExNwcQEChd+/jI1VSsnKcspisKkSe6ULm3CoEFbmTLlALduPWbhwtbo6Lz6O5fDQDWnXLly1K1bN+2zlpYWXbt2BdQUzx0yEQdeTtusr6+fYdrm4cOHA6+mbR4xYgTw+rTNAImJibi45L4VAvNf8NfSgnbt1PfPhrG++OfrXqmpz/989kpJefWVnJz+fVKS+mdiIiQlkZKQiJKYiFZqKsTFqa8XrMIOL2qRgA7NCGbt43WY7k546SLuwIYFwIKMr1FbG8zNoVixzL3MzdWnEynTBgyoScmShejadT2LF5/hzp0Y1q3rnG6R+XdNwZ1fve0OPbsYGxu/cXtmntbye9rmN8l/EUFPD/7+W2On77/kGP5XItBOTUEvOYnGFQozr2M1RFwc0+ed5avZ5wEY0rokc/pVRyehFcTEQGwsxMSQ/CSKi6FHCQw7ik50HIUToJQoRAVtc4zjU1GiouDxY7h3T31lVtGir1YKJUtC6dJQpgyULQvlyqlPFhIArVvbsHdvXzw9/2Lbtqu0bLmSzZu7U7iwGiAyGgZaEIN/bpGamsr69evp1q0bf/31Fw0aNMiS4z5Lz9yoUaMM0zZ7eHi8krZ56NChBAcHU6lSJWJjY7l582baAjO5Rf4L/rlEipY2cXraxBQyJbl4SYYN28bChedRFPjpp2aMGpXxY6AOUB2wTorj1xO/Mu3QtKcJ46JxtXRliscU3Eq5wP37EBGRudfDh89fV668ueBFikD58mpF8OKfVatC5crP+04KiLp1yxAQ8AmNGy/nwIHrNGmynB07elG0qCGulYux7sTNtAlgchioZhkbG3Px4kVq1qyJqakpa9asyZLjDhkyhEGDBmFvb4+Ojk66tM1eXl44ODhQo0aNDNM2JySoT/VTp07NdcFfzvDNAi+2+wIMXXmKxJRU9LS1+LGdPQsnHWDbtqvo62uzYkUHOnWqmuljxyTGMO/YPGYcnsHDuIeAOn9giscU6pWtl7mDpKTAgwevVgq3b8OtW3Djhvq6fh3i419/nCJFoE6d5y9nZ7VJqQAIDX1EkybLCQuLxN6+OH5+vSlRolCBbfOXM3xzB42nd1AUpQUwB9AGfhdCTHtpez/gB+DW0x/NE0L8/qZj5pXgn9H0f1CbBKqZGTNjtB+nTt3G3NyQTZu6U69e2fc6z+OEx8w5Moef/v2JqIQoAFpUasEUjynUKvXWv+fMEUJtSrp2Ta0Irl9X34eFwenTcOfOq9+pVAnq1n1eIVSvrja95UO3bj2mSZM/uXz5PpUrF2XPnj6ULWuq6WJphAz+uYNGg7+iKNpAENAUuAkcB7oLIQJf2KcfUEsIMSyzx80rwd/b9wLL/32+AHMfl3L4tLUjMDCCVq1Wcv16FBUrFmH79p5ZMmP3UdwjZv47k9lHZxOdqI4dbmPTBh93H6qXrP7Bx38tIdSng6NHn79OnHj1SUFfH5yc0j8hlC+fb5qL7t2LoXnzFZw5c4dy5UzZvbsPlSoV1XSxcpwM/rmDpoO/CzBJCNH86eevAIQQ/3thn37k0+Cf0Z2/XkQc7dqtITIynjp1SrN5c3eKFXvzyIR3dT/2Pj8c+oGfj/1MXLI6oqhT1U5Mdp9M1WKZb1b6IElJcP7888rgyJGM+xSKF09fGdSurQ5XzaMiI+Np2XIlR47cpGTJQuze3Ztq1Yprulg56tKlS9ja2sr5DxokhODy5csaDf6dgBZCiP5PP/cG6rwY6J8G//8BEahPCaOEEK8sgKsoykBgIIClpWXN69evv7xLrvFyO/+z9/fP3qNfP18SE1No396WFSs6pBsemNXuRt9lWsA0FpxYQEJKAgoK3e27863bt1iba6CD6dEjOH48fYXw4KXFbRQFqlR5XhnUrQvVquWp4ajR0Ym0bbuavXvDsLAwYt++vgWqAggLC8PExARzc3NZAWiAEIIHDx7w5MkTKlSokG5bTgb/zkDzl4K/sxBi+Av7mAPRQogERVEGAV2EEI3edNzcfOef0d1+kyrFmT79EF99tQeAESOcmTmzeY7l4L/1+BbfH/ye3079RlJqElqKFn2q92Fiw4lYFdHgco9CQGho+srgzBl1XsSLjIygVq30FULp0popcyY9SwOxY0cwJUoYs39/P2xsLDRdrByRlJTEzZs3iX/TAAEpWxkYGFCmTBl0ddPfXOaqZp+X9tcGHgoh3vjcn5uD/8vt/L2cy3J/VzgLF55EUWDmzOZ8/nndNxwh+1yPvM7UA1NZcmYJKSIFHS0dvGp4MaHhBCxNLTVSplckJKgVwLPK4OhRtYJ4WenS6SuDmjXhLRN7clpcXBJt2qxm9+5QSpUyYf/+fgWyD0DKPXIy+OugNuU0Rh3NcxzoIYS4+MI+Hwkhbj993x4YJ4R4Y3TMzcH/xTt/vVQocvQexw6EY2Cgw4oV7enYMYfa3N8g5GEIPgd8WHFuBakiFT1tPQY4DeBr168pZVJK08V7VUQEHDv2vEI4dgyiotLvo60NdnbPK4M6dcDWVuOpLmJjk2jVaiX791+nbNnCHDjgRfny77d8pyR9qJwe6tkKmI061HOxEOI7RVF8gBNCiE2KovwPaAMkAw+BwUKIy286Zm4O/qBWAFuPhLPlh6OEXH6AhYURvr7d3nsoZ3a5fP8yk/dPZs2FNQgEBjoGDK41mPENxlPcOBe3UaemQlBQ+qeDc+fUOQsvKlxY7UB+cbhp8Zy/rujoRJo3X8HhwzeoUMGM/fv7FdhhoJJmyWUcs9nZs3f4+OO/uHXrCdbW5mzb1oOKFXPv4/6Fexf4dt+3bLy0EQAjXSOGOw/ny3pfYm6URyZqxcbCqVPpK4Qbr4wbUIeWvvh0UKuWmnU1m0VFxdOs2QqOHbtFpUpF2b+/H6VKmWT7eSXpRTL4Z6NNm67Qo8cGYmKScHW15O+/u2JubqTpYmXK6dun8d7nzZagLQCY6Jkwqu4oRrmMwswgDzZV3L6dvjI4flzNlfQic3M102vXruDmlq2jih49iqNx4+WcPn0HW1sL9u3rS4kSGa8wJUnZQQb/bCCE4IcfDjN+/G6EgJ497fnjjzbo6+edIYrPHL15FO993uwK2QWAmYEZY1zGMLLOSEz08/DdakoKBAY+rwwOHlSbj54pXhw6doQuXcDV9bXrLnyIBw9i8fBYxvnz97CzK46/f18sLPLGzYGU98ngn8USEpIZOHALy5efBeC77xrx1VcN8vwY54DwALz9vfG/pq5OZG5oztj6YxlaeyjGerlrZM17EQIuXIC1a2HNGrh69fm2kiWhUyf1iaBevSztOL53LwYPj2UEBkZQo0ZJ9uzpQ9Gihll2fEl6HRn8s9C9ezG0b7+Gw4dvYGSky4oV7WnfPn9NbfcP82ei/0QO3TgEQAnjEoxvMJ5BtQZhoGOg4dJlESHg7Fm1Eli7Nv3w0tKloXNn9Ymgbt0sSUdx+/YT3NyWcvXqQ2rVKsXu3b0xNX3+uyyoSeGk7CWD/wd69h+zdKoW33++i/DwKMqUKcymTd1wdPxIY+XKTkIIdoXsYqL/RI7/dxyAUial+Mb1Gz51/BR9HX0NlzALCQEnT6qVwNq1ahK7Z8oAiuiWAAAgAElEQVSVg759oV8/eGn25Lu6efMxbm5LCQ19RN26Zdi1qxcmJvoZThSUFYCUFWTw/wB+gXcZuvIUkZcfcH9zGCIplTp1SvPPP90oWTL/d94JIdgStAXvfd6cuXMGAEtTSyY2nEjf6n3R1c7+kTM5Sgh1XsGaNbBuHdy8+Xybhwd4ean9BEbv125//XokDRsuJTw8CldXS7Zv78n03UEZJgSUpA+V2eAvF4LNwMoj14gIuEXExhBEUipWdUuxb1+/AhH4QV2arrVNa04OPMn6zuupVqwa4VHhDNg8ANv5tiw/u5zk1GRNFzPrKIo6JHTmTPUJYM8e6NlTXXfZ3x/69IGPPoLhw9XO5HdUrpwZ/v59KV3ahIMHw2nTZjXOZYtgqKt2NsuFYCRNkHf+L4mKiseh0VLCT90FwMy1FG37ObD0kzo5XpbcIiU1hbUX1zJp/ySCHqgjZ2zMbfjW7Vu62nVFS8mn9xBRUerTwOLF6sihZxo2hMGD1eGj77B2wdWrD2jYcCl37kTTvHlFhv/Pg6Phj2Sbv5SlZLPPe7hw4R4dOqzh6tWHaOlrY+5ZATOboszv6ST/cwLJqcmsPLcSnwM+hD5SO0vtitsx2X0y7W3b5/mRT2909iz8+iusWAHR6joKFC8On34KgwaBZebyJl26FIGb21IiImLx9LRmw4Yu6OmpTwCyA1jKCjL4v6O//jrPgAGbiY1NwsGhBKOmNSI0MVH+R8xAUkoSS88sZcqBKdx4rM6wdSzpyGT3yXhae+bvSuDxY1i5EhYsUNcyAHWuQI8eMHasmnvoLc6fv4uHxzIePIijQ4cqrFnTCf+gCNkBLGUJGfwzKS4uia6f+LJ5tZqHrndvB3791TNbc/DnFwnJCfxx+g+mHpjK7ejbADiXdsbH3YdmFZvl70pACDh8GObPV0cLPcs51Lo1jBsH9eu/8eunT9+mUaPlREbG069fDcq0seLPo+Fp22UHsPS+ZIdvJpw/f5cqDgvUwK+lUKJleXqNc5GBP5P0dfQZUnsIISNCmNlsJsWNi3Ps1jFarGxBw6UN2Xdtn6aLmH0URQ3wf/0FwcEwbJjaQbx5MzRooM4e3rpVrSQy4Oj4Edu29cDISJelS89w8e+rGOio/x1lB7CUEwpk8BdCMG/eMWrX/o3rwY/QKWrAR32qYOBgQUDwfU0XL88x1DVklMsoQkeEMr3JdMwNzQkID8BjmQeNlzfmUPghTRcxe5UvDz//rI4UmjABzMwgIAA8PdUF7VeuhORXR0e5uJTln3+6oqurxd9/nqdOZCp9XMoxt7sjZ25E0nzWfn7YmcGymJKUBQpcs09ERAyffLKJLVvUUSstO9oSUrEQCQqyrTWLPE54zNyjc/nx8I9EJag5+ZtXbM4UjynULl1bw6XLAU+ewKJF6tDR//5Tf1a+PHzxhTpn4KX5Ahs2BNKly3pSUwWzZzcn0bYI8/2D07YP9ajEl81tcvACpLxMtvlnwM8vhD59/uHOnWjMzAz47bfWdOpUVY6yyCaR8ZHM/Hcms4/M5kniEwBaW7fGx8OHGiVraLh0OSAhQR0dNGPG8+RyxYrBiBEwdCgUKZK26+LFp/n0000A2HW35Ynl8zklNiUKsXOUW44WXcq7ZPB/waNHcXz5pR9//HEagIYNy7FiRXu52EYOeRD7gB8O/8DPx34mNikWgI5VOjLZfTLVilfTcOlyQEoK+PrC//4Hz/5NFyoEn30Go0dDKXVltZkz/2XMmF1oaSmYt6uIUWU1xba885fehQz+T23ceImhQ7dx5040enraeHs3ZPz4Bjm2sLr03N3ou0w/NJ1fjv9CQkoCCgrd7LoxyX0S1ubWmi5e9hNCnTE8bRr4+ak/MzBQO4vHjQMLC775Zg/ffx+Ajq4WNQY40KWNrQz80jsp8MH/9u0nDBu2nY0bLwFQv35ZfvutNVWqyFEUmvbfk//4/uD3LDq5iKTUJLQULXo79MbbzRurIlaaLl7OOHkSvv8eNqorq2FiAmPGID7/nMHjDrJw4UlMTPTYv79fvk0kKGWPAjvUUwjBH3+comrVX9i48RKFCukxb15LDhzw4qZIxdv3An6BdzVdzAKtlEkp5rWaR/CIYAY4DUBL0WLZ2WXYzLNh4OaBhEeFv/0geV3NmrBhg9oM1KKF2kk8aRJKxYrMtwmmS6cqPHmSSIsWK7l69YGmSyvlQ/nuzv/s2Ts4Oi5ECGjVqjILFnyMpaWpTKGbi4U+CsVnvw9/nvuTVJGKnrYeA5wG8LXr15QyKaXp4uWMgwfh66/VIaJAYvmKeBYehN+5GMqXN+PQoU/kesBSphTYO//q1UvyzTeurFzZgS1bumNpqXbqHrwaQVySOgszLimFg1cjNFlM6QVWRaxY2m4pgUMC6W7XnaSUJOYfn0/FuRUZvXM092LuabqI2c/VFQ4cgE2boEoV9K6FsPHcNzgXfsK1a5E0b76CR4/iNF1KKR/Jd8EfYMqURvToYZ8uvYBr5WIyhW4uZ2Nhw18d/+Lc4HN0rNKR+OR4Zh2ZRYU5FRi/ezwPYvN584eiqOkhzp2DhQspVLQQWx8vwFa5z4UL92j98UpiY5M0XUopn8h3zT5vIsfz5y1n7pzB29+bzUGbATDRM+Hzup8z2mU0ZgZmGi5dDoiIgLFjubF0I/X5lBuY0sq5CP8EDEVXN+sXnpfyhwI/2kfKP47dOoa3vzc7Q3YCYGZgxhiXMYysMxIT/QLQDn7gAJc/GUuDEDceYESv8lEsO/QlWqXkKCDpVQW2zV/Kf5xLO7Oj1w4CvAJoVKERkfGRTPSfSIU5FZhxaAYxiTGaLmL2atgQ28ADbBtiijGJrLhmypgKgxDz5j3PJipJ70je+Ut5jn+YPxP9J3LohpowrrhxccbXH8+gWoMw1DXUcOmy1+4/D9Gq7y6ShBbfsYeva8XBwoXg5KTpokm5RIG+8/cLvCvH8+djHhU8OOh1kJ29duJc2pl7MfcYvWs0lX6upM4eTk7QdBGzTZPe9Vm5uguKAt/QmEUnBNSuDSNHqgvNSFIm5bs7fzmev2ARQrD16la8/b05fUfN3WRpaskE1wn0q9EPXe38uTbDwoUnGDRoKwqCtVob6JR6QV1kfvZs6NxZHTkkFUgF9s5fjucvWBRFwdPakxMDT7ChywaqFatGeFQ4A7cMxHa+LcvOLCM59dVc+nndZ5/VYsoUDwQKPbW7sKdKK7h9G7p2hZYtISRE00WUcrl8F/zleP6CSUvRokOVDpwbfI5VHVdhY25D6KNQ+vn2w+4XO1adX0WqSNV0MbPUN9+4MmKEM4lJqbS70YATX/2sponeuROqVYMpU9S00pKUgXzX7ANyPL8EyanJ/HX+Lybvn0zoo1AAqhWrxmT3ybSv0h4tJX/c96SmCvr0+ZuVK89jYWHEwX/aYLvoe1i+XN3B2lpdbL5RI80WVMoxcpy/JAFJKUksO7uMKQempCWMq1GyBj7uPnhae+aLReaTklJo23Y127cHU7ZsYQ4f/pQyV0/CkCFw+bK6U8+e8NNPUELeDOV3BbbNX5JepKutS3+n/gQNC2J+q/mUMinFmTtnaLO6DXX/qMvO4J3k1hugzNLV1Wbdus64uJThxo3HNGv2Jw8c6sDZs/Ddd+qaAStXgo0NrFmj6eJKuUSWBH9FUVooinJFUZRgRVHGZ7BdX1GUNU+3H1UUpXxWnFeSMktfR58htYcQPDyYWc1nUdy4OMduHaPFyhY0XNqQfdf2abqIH8TYWI8tW3pQrVoxLl26z8cf/0V0Imqm0IsX1bTRUVHQrRsMGgRxMklcQffBwV9RFG1gPtASqAp0VxSl6ku7fQo8EkJUAmYB0z/0vJL0Pgx1Dfm87ueEjghlepPpmBuaExAegMcyDxovb8yh8EOaLuJ7K1rUkJ07e1GunClHj96iY8e1JCamgJUVbNsG8+eDnp46KaxuXbhyRdNFljQoK+78nYFgIUSoECIRWA20fWmftsCyp+/XA42V/NDYKuVZxnrGjK0/lrCRYUzxmIKZgRl7w/bSYEkDWqxowfFbxzVdxPdSunRh/Px6U6yYEbt2hdCnz9+kpKSq4/6HDIEjR6BSJTVzaM2a6gLzUoGUFcG/NHDjhc83n/4sw32EEMlAFGCeBeeWpA9iom/ChIYTCBsZxsSGEzHRM2FnyE6cf3emzao2nLlzRtNFfGeVK5uzY0cvTEz0WLPmIiNGbH/er+HoCKdOQffuEBMDvXvDp59CbKxmCy3luKwI/hndwb/cg5aZfVAUZaCiKCcURTkRESEnZ0k5x8zADB8PH8JGhjGu/jiMdI3YHLQZx4WOdFrbiQv3Lmi6iO/EyekjfH27oaenzS+/nGDy5P3PN5qYqB3AixapncGLF4OzMwQGaq7AUo7LiuB/Eyj7wucywH+v20dRFB3AFHj48oGEEIuEELWEELWKFZOTs6ScZ25kzrQm0wgdEcqouqPQ19Znw6UNOCxwoMeGHly5n3fayT08KrB6dUe0tBQmT97PvHnHnm9UFBgwAI4eVUcBXbwItWrB0qUaK6+Us7Ii+B8HKiuKUkFRFD2gG7DppX02AX2fvu8E7BV5fXydlK+VKFSCmc1nEjoylKG1h6KjpcOqC6uo+ktV+v7Tl5CHeSN9Qvv2VVi0yBOA4SO289WMgPQ7ODioi8j37q2OAPLygr59ITpaA6WVctIHB/+nbfjDgJ3AJWCtEOKioig+iqK0ebrbH4C5oijBwGjgleGgkpQblTIpxbxW8wgeEcwApwFoKVosP7sc2/m2DNg0IG3iWG5m6VKaYo3KgoBpX+1l6oJj6XcoVEidEbxkCRgZqe9r11Y7haV8S87wlaR3EPooFJ/9Pvx57k9SRSq6WroMrDmQr12/ppRJKU0XL0PevhdY/u91Hu27yeOjd9DR02KPXx8aNiz36s6BgdCli9oMZGAAc+aozUNycF6eIWf4SlI2sCpixdJ2SwkcEkgP+x4kpyYz//h8Ks6tyOido7kbnfvWkHiW7NDMrTRmjsVJTkyldetVnDp1+9Wdq1aFY8fUEUDx8fDZZ9Cjh1wrIB+Sd/6S9AEu3rvIpP2TWB+4HgAjXSOG1R7G2PpjMTfKPaOZnyU7rGdlzpKph1i79qKaCO6gF7a2Fhl/aeVKNfjHxKhzA9auVYeKSrmaTOwmSTnozJ0zfLvvWzZdUcc6FNIrxOd1PmdMvTGYGZhpuHTpJSaqieB27AimTJnCHDr0CZaWphnvfOWK2gx07pw6O3jWLBg8WDYD5WKy2UeSclCNkjXw7ebLsf7HaFGpBdGJ0Uw9OJXys8szZf8UHifknmYTPT1tNmzoQv36Zbl58zFNm/7JvXsxGe9sY6POCh40CBITYehQtTKIjMzZQktZTgZ/ScpCtUvXZnvP7QR4BdCoQiOiEqLw3udNhTkVmB4wnZjE1wTZHGZkpMuWLT2oUaMkQUEPaN58BZGR8RnvbGiorgmwZo06QWz9enVS2LN00VKeJIO/JGWD+pb12dNnD/59/Wlg2YCHcQ8Zv2c8VnOtmPXvLOKSNJ9V08zMgB07elK5clHOnLlD69ariI1Nev0XunRRU0NUrw5Xr0KdOrB9e84VWMpSMvhLUjZyL+/OgX4H2NlrJ86lnbkXc4/Ru0ZTcW5F5h2bR0KyZpdZLFGiEH5+vSlTpjABAeF06vQ0E+jrVKoEhw5Bx47qCCBPT3WRmFzadyi9ngz+kpTNFEWhWcVmHPn0CJu7b8axpCO3o28zfPtwKv9cmd9O/kZSyhvuuLNZuXJm+Pn1xsLCiO3bg59nAn0dY2N15M/kyZCaCl98ofYFpLyh0pByHRn8JSmHKIqCp7UnJwaeYEOXDdgVt+PG4xsM3DIQ2/m2LDuzjOTUZI2UzdbWgh07eqZlAh06dNubVzjT0gJvb1i3DvT11T6Bjh1ldtA8RAZ/ScphWooWHap04Oygs6zquAobcxtCH4XSz7cfdr/Yser8KlLFG+68s0nNmqXYvLk7BgY6LFx4kq+/3vP2L3XqBLt3Q5Ei4OsLjRvD/fvZX1jpg8ngL0kaoqVo0c2uGxeGXGBZu2VYFbHiyoMr9NjYA4cFDmwI3JDjlYCbW3nWreuMjo4W06YdYsaMTKxs1qCB2g9gaakOC61XD0JDs7+w0geRwV+SNExHS4c+1ftweehlfmv9G5amllyMuEindZ1wWujEpiubcnSReU9Pa5Yta4eiwLhxu/ntt5Nv/1KVKvDvv89HArm4wMlMfE/SGBn8JSmX0NXWpb9Tf4KGBTG/1XxKmZTi7N2ztF3dljq/12FH8I4cqwR69LBn/vxWAHz22RbWrr349i+VKgUHDkCTJnDvHri5wY4d2VxS6X3J4C9JuYy+jj5Dag8heHgws5rPorhxcY7/d5yWK1viusSVvWF7c6QcgwfX5rvvGiEE9Oq1kR07gt/+pcKFYetWdX2AmBh1KOiSJdlfWOmdyeAvSbmUoa4hn9f9nNARoUxvMh1zQ3MO3ThE4+WN8VjmQUB4wNsP8oG++qoBY8a4kJSUSocOawgIyMT6BXp6sGwZfPWVOvzzk09gyhQ5FyCXkYndJCmPeJLwhDlH5/DTvz8RGa/m1mlWsRlTPKbgXNo5284rhGDAgM388cdpTE312bevHzVqlMzcl3/5BYYPV+cDDBigftbRybaySjKrpyTlW5Hxkcz8dyazj8zmSeITADytPfFx98Hxo+xJuZySkkq3bhtYvz6Q4sWNOXjQC2vrTKas9vWFbt3U9QE8PWH1anWimJQtZFZPScqnzAzM8PHwIWxkGOPqj8NI14gtQVtwWuREx7UduXDvQpafU1tbixUr2tOsWUXu3YuhSZPlXL+eycyebdvC3r1gbg5btkCjRmqHsKRRMvhLUh5lbmTOtCbTCB0Ryqi6ozDQMWDjpY04LHCg+4buXLl/JUvPp6+vw8aNXahXryw3bjymSZM/uX37Sea+7OKizgWoUEFdKaxePQjORAeylG1k8JekPK5EoRLMbD6TkBEhDK09FF1tXVZfWE3VX6rS95++hDwMybJzGRvrsXVrD5ycPiI4+CFNm/7JgweZTOlgYwOHD4OTE4SEqJPDLl3KsrJJ70YGf0nKJ0qZlGJeq3lcHX6VAU4D0FK0WH52OTbzbBiwaQDXI69nyXnMzAzYubMXVasW4+LFCJo3X0FU1GvWAnhZyZKwb5+aBuLuXXB3VxeLl3KcDP6SlM9YmlqyqPUirgy7Qt/qfREIfj/9O5V/rsyQrUO49fjWBx3fL/Aucw+FMnF+CypWLMLJk7dp2XIlT55kMj21iQls3gxNm6pt/x4ecCHr+ymkN5OjfSQpnwt6EMTk/ZNZdX4VAoG+tj6Daw1mfIPxlChU4p2O5Rd4lxGrThOXlIKhrjbfuFXim0+3EB4ehaurJdu398TYWC9zB4uLg/btYedOsLCAPXvAweE9rlB6kRztI0kSANbm1qzssJLzg8/TqWonElISmH10NlZzrRjnN477sZnPwnnwagRxSWre/rikFIJi4vH370vp0iYcPBj+9tXAXmRoCP/8A61aqZlAGzWCs2ff5xKl9yCDvyQVENWKV2Nd53Wc/uw0bWzaEJsUy4zDM6gwpwIT907kUdyjtx7DtXIxDHW1ATDU1ca1cjGsrIrg79+XkiUL4e9/jfbt1xAfn8l1CQwMYONG+PhjePBArQBOn/6Qy5QySTb7SFIBdfzWcbz3ebMjWE2+ZqpvyhiXMYysO5LC+oVf+z2/wLscvBqBa+VinLkRye7AOzSpWhJPy6K4uS0lIiKWVq0qs3FjF/T1MzmbNyEBOndW+wKKFFHXCHByyorLLHDkDF9JkjLlUPghvPd5pyWMK2pYlLH1xjLMeRjGeq+fifvDzivM938+Vn+oRyValDLDw2MZDx7E0batDevWdUb36ZPCWyUmqovE+/qCmRn4+UGtt8Yw6SWyzV+SpEypb1mfPX324N/XnwaWDXgY95Dxe8ZjNdeKWf/OIi4pLsPv7Q6888pne/sS7N7dhyJFDPD1vULPnhtJTs7kgjR6eurawO3bQ2Skmhr6+PEPvTzpNWTwlyQJAPfy7hzod4CdvXbiXNqZezH3GL1rNJV+rsT8Y/NJSE4/lLNJ1ZIZfq5RoyS7dvWmcGF91q0LpG/ff968IPyL9PRgzRp1PeCoKLUCOHo0S65PSk82+0iS9AohBFuvbsXb35vTd9QOWEtTSya4TqBfjX7oausCatPPszb/L5vbpDvGkSM3adr0T6KjE+nbtzqLF7dFS0vJXAGSkqBnT3WBeBMTdTioi0uWXmN+Jdv8JUn6YEII/r78N9/u+zYtYZxVESu8G3rT06EnOlpv7tANCAinefMVxMYm0b+/IwsXts58BZCcrC4Ks3q1WgHs2KHmBJLeSLb5S5L0wRRFoUOVDpwddJZVHVdhY25D6KNQ+vn2o9ov1Vh1fhUpqSmv/X6DBpZs2dIdAwMdfv/9NMOHb8v8UpQ6OvDnn9CjBzx5As2bq8nhpCwhg78kSW+lpWjRza4bF4ZcYFm7ZVgVsSLoQRA9Nvag+q/V2RC4gVSRcbu+h0cFfH27oa+vzS+/nGD06J3vVgEsXw69ekF0tDohTM4DyBIy+EuSlGk6Wjr0qd6Hy0Mv81vr37A0teRixEU6retEzUU12XRlU4aBvVmzimzc2BVdXS1mzz7K+PG7M18BaGvD0qXqPIDHj6FZM7h8OWsvrAD6oOCvKEpRRVH8FEW5+vTPIq/ZL0VRlDNPX5s+5JySJGmerrYu/Z36EzQsiPmt5lPKpBRn7pyh7eq21Pm9DjuCd7wS3Fu1qsy6dZ3R0dFixozDTJzo/24VwIoV0LKlmgqiSRO4di3rL6wA+dA7//HAHiFEZWDP088ZiRNC1Hj6avOB55QkKZfQ19FnSO0hhIwIYXbz2ZQwLsHx/47TcmVLXJe44h/mn27/tm1tWb26I9raCt99d5CxY/0yXwHo6cH69eDqCrduqRXA7dvZcFUFw4cG/7bAsqfvlwHtPvB4kiTlQQY6BoysO5KQESHMaDIDc0NzDt04RKPljWi0rBEB4QFp+3bsWJU1azqho6PFjz/+y7Bh20hNzWQFYGSkLgVZs6a6IEyzZvDwYTZdVf72ocG/hBDiNsDTP4u/Zj8DRVFOKIpyRFEUWUFIUj5lrGfMl/W/JGxkGFM9pmJmYIb/NX9cl7jSfEVzjt06BqgVwD//dE3rBO7ff1PmJ4IVLqwO+6xSRV0HoFUrtTNYeidvHeevKMpuoGQGm74BlgkhzF7Y95EQ4pV2f0VRSgkh/lMUxQrYCzQWQryytpyiKAOBgQCWlpY1r1/PmpWHJEnSjMj4SGb9O4tZR2bxJFFd79fT2hMfdx8cP3Jk9+5Q2rZdTWxsEt262bF8ebvM5wK6dUtdCvLaNXUY6KZNatNQAZcjk7wURbkCuAshbiuK8hGwTwhh85bvLAW2CCHWv2k/OclLkvKPB7EP+PHwj8w9NpfYJHXN3w5VOjDZfTKPrpjw8cd/8eRJIu3aqX0Cmc4GGhSkVgAREdC9u9oprFWwBzHm1CSvTUDfp+/7Ar4ZFKSIoij6T99bAPWBwA88ryRJeYi5kTn/a/I/wkaGMcZlDAY6Bmy8tBGHBQ78cm88f6x3w8zMgH/+uUy7dmuIi8vkgjDW1rB9OxQqBKtWwRdfZO+F5CMfGvynAU0VRbkKNH36GUVRaimK8vvTfaoAJxRFOQv4A9OEEDL4S1IBVNy4OD82+5ElLY5S26IHOlq6rL6wmm5HXGngHUwRc3127Ajm44//Ijo6MXMHrVkT/v4bdHVh1iyYOzd7LyKfkLl9JEnKUS+uA6yj+xAbaz92XVtFcmoyWhElMPhrILGPtKlXryzbtvXA1NQgcwdeuVKdCawoamXQtm32XkguJXP7SJKUK724DnByUlFqFh7LlWFX6FejHxSPILbXfDCN4vDhGzR0/4MHD2Izd+CePWHKFBBCbf+XawG8kQz+kiTlqAzXAS5ixZK2S7g09BI93FuA1xIo8pBzZ+5jU2sa50NDM3fwb74BLy+Ii4PWreUs4DeQzT6SJOW4F9cBblq1xCvbL967yLi/p7J1gjncL4Zi8YDP5hgypd2XWBhZvPngSUlqGog9e9S5AIcPq8tCFhAyn78kSXne3gtHae+5kcfXjcDsEUb91zOqZT/GuIyhiGGGqcRUkZHqENCLF8HDQ50UVkDmAMg2f0mS8rxGdnUIOzWJqtVNIbIIsb9247u1f1BhTgV89vvwOOFxxl80M4OtW6FkSfD3h4ED1b4AKY0M/pIk5WpFixry74HBNG5cAaJN0F7Wn6hAc77d9y0V5lRgWsA0YhJjXv1iuXJqHiAjI1i2TO0MltLI4C9JUq5XuLA+27b1pEcPe1LiddBe1RfrW514GPeQr/Z8RYU5FZj570zikuLSf7FmTXUZSC0t+PZbdWEYCZDBX5KkPEJPT5s//2zPl1/WIyVZEPSbHZ/GLMa5VB0iYiMYs2sMFedWZN6xeSQkJzz/YuvWMGeO+r5/f7UZSJLBX5KkvENLS2HGjKbMnt0cRYE/fgin5umJ+HbejNNHTtyOvs3w7cOp/HNlFp1cRFLK0zQRw4bB55+rI4Hat4dLlzR7IbmADP6SJOU5I0fWZc2aTujpabNgwQl+/SKKvV0PsbHLRuyK23Hj8Q0+2/IZNvNsWHpmKcmpyfDjj9CuHURFgadngV8HQAZ/SZLypM6dq7F3bx8sLIzYvj0YV9clOBp6cHbQWVZ3XI2thS1hkWF4+XpRdX5VVl5cTcqfy8HREUJDoUcPSEnR9GVojAz+kiTlCX6Bd/H2vYBf4N20n9Wvb8mRI59iY2PO+fP3qF37N3b7hdHVrisXBl9gebvlVCxSkasPr9Lr7144LK/L1un9EebmsHMnTJyowSvSLDnJS5KkXO/FZHCGutrM7V8EiNoAAA6LSURBVO6YbmZwZGQ83bqtZ+fOEBQFJk5siLe3G9raWiSlJLH87HKmHJjC9Sh1gaj+j6xYOO8aWimpsG4ddOqkqUvLcnKSlyRJ+caLyeDiklI4eDUi3XYzMwO2bu2Bj487AD4+B2jefAV370ajq63Lp06fEjQ8iAUfL6C0SWl+LxLKmCbqspHJfXsjzp/PycvJFWTwlyQp18soGdzLtLW1mDjRDT+/3hQvbsyePWE4Oi7kwAH1bl9PW49BtQYRPCKY2c1ns6pxcVbYg05sPLeaOHPgzCtrUeVrMvhLkpTrNa1agrndHenjUu6VJp+XNW5sxenTn+Hqasnt29E0arSM6dMDSE1Vm7gNdAwYWXckoZ+HETFrKudKaVPmXjzRndvReIk7B68fzKnL0ijZ5i9JUp71puygycmpTJiwl+nTDwHg6WnNsmXtKFrUMN1+0VcuoFWnDkZRsXznChMaQ1OrpkzxmEKdMnVy7FqyiszqKUlSvvZiJ7Cethb1K5nTo065VyqBLVuC6NPnbx49iqdcOVPWru2Ms3Pp9AfbswfRrBlKaiq9ehiw0joegI8rf4yPhw9OHznl1GV9MNnhK0lSvvZiJ3BiSir+/2/vzoOrKu8/jr+/CQkhCagZFlksUFEgahEMqAiiUhCQKZUBhvKDIGgpHUE2pVgVKrbUwpRW/GEt+IMOEQUREafCD5eKgKVCWAJhMyBEadCwNBTZSZ7+cQIEkkAW7jnc3s9r5g7nJmf5PsPkc899znOes2M/Q9PSmbJsxwXrde9+M+vX/4zWreuRnX2Ydu1m8eKLqzh58sz5lTp2xKZMASBtcTRT6z9KfEw872e9zx0z7qDn/J5s/va/66Kwwl9EwlLRi8Bn5Tt49dNdF9wLANCo0bWsWjWY4cPbcPp0AU8//THNm0/n7be3cq73Y9Qo6NcPO3qUUZNXsGfgRkbfNZq4KnEs2r6IFq+2oO/bfdl+YLtfTQwphb+IhKWzF4Hvb1rrgiDLL3DFhoKCNzHctGldWbasP8nJtdi9O4/evRfQocNfWLcux3vw+8yZ0KIFZGVRa8hIft9pCrue2MWw1sOIiY5h/pb53PLKLaQuSmXXoV3+NTYEFP4iErY6Jddh9qA2/Pz+JkRHGVD6UNCzOne+kYyMobzySjdq1oxn5cqvSEmZycCB7/LPf52BRYsgKQmWLIEJE6hXvR4vd3uZrOFZDGk1hCiLIm1TGk3/tymPvfcY2XnZfjX3itIFXxH5r3C55wKXJC/vBJMmreSllz7n1Kl84uNjGDu2LU+2OknCj7tBQQG88443E2ih3f/azQsrXmBOxhzyXT4xUTE81uoxnmn/DPVr1L/E0fyh0T4iImW0a9chfvGLj1i40JvquX796vy2/Un+Z94zRCUmwOefQ3LyBdtkHczi+U+f543Nb+BwVI2uytCUoYxrN47rE68PohmAwl9EpNxWrMhm1KhlrF+/D4CUpOP84dCbtLs5DtLToXr1Ytts3b+VXy3/FQu2LgCgWpVqDGszjLH3jKVmfE1f6weFv4hIiS7XPVRQ4EhLy+CXv/wbOTlHAOjMTga0TaTH0j9QvUZcifvN+CaDCcsnsHiHN01EYmwiI+4cwZi7x3BdtetC16CLKPxFRC5yudlBizp69BSTJ3/GlMmfcfyEdz9BtVijR89b6NfvVh58sAmxsdHFtkvPSWf8J+NZunMpANdUvYbRd49m5F0jqVG1xiXrc86xbdsB8vMLuO22sl23uJjCX0TkIuMXZzJn9fnROal3N2Rij1svuc2BA8dYMGYWb8xZzyoanvv5ddfF0atXMq1a1aVevernXrVrJ1ClShSrv17Nc588x8e7PwYgqVoST7V9imFthpEYm3huP3l5J1i5MpslS7JYunQn2dmH6dmzOQsX9qlQGxX+IiIXKc+ZfzGDB5M9+x3m1bqfN+p0YlNm8XsJwHvOcO3aCec+DKz6ETKOfsZXBVsg/hiJZ+pwV/XOxB36Hps37Sc7+/AF29esGU+fPslMn/5Qhdqo8BcRKUFFhoQCcPQotGkDW7dCaiqZT01h8eLt7NmTR07Od+TkHGHfviPk5h6lPLFatWo0LVvWpUuXG+na9SZSUuoRVXjPQkUo/EVErrQtW6B1azh+HGbPhkceKbbK6dP5fPvtUXJyjpz7QDi7fPDgcY7HHGTT8c/4pmomXP8N9RvH8dx9zzCo5SBio2MrXaLCX0TkCij2TWH2bBg8GOLjYe3aYuP/y8I5x7vb32XC8glszvUmjGt0bSPG3zueAS0GUCWqSoXr1ayeIiKVdPYawZzV2Tzx5gZvwrhHHoHUVDh2DHr39rqDysnMeLj5w2wcupH5vebTrGYz9uTtYfB7g0menszcTXMpcAVXvkFFVCr8zay3mW0xswIzK/WTxsy6mNkOM9tpZuMqc0wREb+U+OxgM5g+HZo18/r/R46s8P6jLIo+t/Qh8+eZpD2cRpOkJmQdymLammkYFe/3L9OxK7l9JtATWFHaCmYWDUwHugLJwE/MrPzfk0REfFbqs4MTE+Gtt8iPrQqvvcbGl/9SqeNER0XT/wf92fb4NkanTKVZtaF8tC23ktVfWsU7lgDn3DbwvsJcQhtgp3Puy8J15wE9gK2VObaISKidnTa6pNFBH0bXJr3DQJ7+cAbfGzeCT39wBx063FbqvopeOwBKXV7yj+YcP53Pmu0byjcUtZwqFf5lVB/4usj7vUD4PRhTRCJSp+Q6JQbwyqz9pLXsTrsv1tA+eyN1xzwOaz/1uoUuUvT+gnlrvDg8lV9wwfKC9L3c9f2kYt1MoQr/y3b7mNlHZpZZwqtHGY9R0teCEocYmdkQM0s3s/T9+0u+gUJE5GrQ/qZaxMXG8ORDI8mLS+TmdSvh1VdLXPfiR06eyi8otnz29yV2M4XAZc/8nXM/rOQx9gI3FHnfAMgp5VgzgBngDfWs5HFFREKmaJfQVzf+nmvH/AzGjIEHHoCmTS9Yt/1NtViQvvfcw+bBC/6iy9Vioul3Z0P63dmwYjehldMVGedvZsuBJ51zxQbmm1kV4AugI/BPYC3Qzzm35VL71Dh/EQkrqamQlgYpKfD3v0NMzAW/Lkuf/5UIe19u8jKzh4GXgVpAHrDROfegmdUDXnPOdStcrxvwRyAamOWc+83l9q3wF5Gwcviw9/zf7Gx49ll44YVAytAdviIifluxAu67z7vou3IltG3rewm6w1dExG/33gtjx3rP/h0wAI4cCbqiUin8RUSupIkT4fbb4csvK3X3b6gp/EVErqTYWJg7F+LiYNYsWLQo6IpKpPAXEbnSkpPhd7/zln/6U9i3L9h6SqDwFxEJhWHDoHNnOHgQHn2Ucj3hxQcKfxGRUIiK8ub+T0qCpUvhT38KuqILKPxFREKlXj3485+95TFjYPv2YOspQuEvIhJKvXrBwIFw4gT07w+nTgVdEaDwFxEJvWnToFEjWLcOfv3roKsBFP4iIqFXowbMmePd+TtpEqxfH3RFCn8REV+0bw9PPAH5+V430MmTgZaj8BcR8cukSdCkCWRmBjbx21kKfxERv8THe8M/zeDFF2HDhsBKUfiLiPipXTsYPtzr/hk0CE6fDqQMhb+IiN8mTYLGjSEj4/w0ED5T+IuI+C0hAWbO9JYnToQtl3ywYUgo/EVEgtCxIwwZ4nX7DBrkdQP5SOEvIhKUyZOhQQNYu9a7EcxHCn8RkaBcc835Cd+efRZ27/bt0Ap/EZEgde8OffvCsWMwdKhvUz8r/EVEgvbSS97Uzx98AK+/7sshFf4iIkGrXRumTvWWR46E3NyQH1LhLyJyNUhNhU6d4NAhXx78rvAXEbkamHkPfklI8F5nzoT0cFVCuncRESm7xo0hKwvq1g35oXTmLyJyNfEh+EHhLyISkRT+IiIRSOEvIhKBFP4iIhFI4S8iEoEU/iIiEUjhLyISgcz5NINceZnZfiD7MqvVBA74UM7VSu1X+9X+yFVa+xs652pdbuOrNvzLwszSnXMpQdcRFLVf7Vf71f6Kbq9uHxGRCKTwFxGJQOEe/jOCLiBgan9kU/sjW6XaH9Z9/iIiUjHhfuYvIiIVELbhb2ZdzGyHme00s3FB1+MnM5tlZrlmlhl0LUEwsxvM7BMz22ZmW8xsRNA1+cnM4sxsjZllFLb/+aBrCoKZRZvZBjP7a9C1+M3M9pjZZjPbaGbpFdpHOHb7mFk08AXQCdgLrAV+4pzbGmhhPjGze4HvgDnOuVuDrsdvZlYXqOucW29m1YF1wI8j6P/fgATn3HdmFgOsAkY45/4RcGm+MrPRQApQwznXPeh6/GRme4AU51yF73MI1zP/NsBO59yXzrlTwDygR8A1+cY5twI4FHQdQXHO7XPOrS9cPgJsA+oHW5V/nOe7wrcxha/wO4urBDNrADwEvBZ0LeEqXMO/PvB1kfd7iaA/fjnPzBoBLYHPg63EX4VdHhuBXOBD51xEtR/4IzAWKAi6kIA44AMzW2dmQyqyg3ANfyvhZxF15iNgZonAQmCkc+7fQdfjJ+dcvnPudqAB0MbMIqb7z8y6A7nOuXVB1xKge5xzrYCuwOOFXcHlEq7hvxe4ocj7BkBOQLVIAAr7uhcCc51z7wRdT1Ccc3nAcqBLwKX46R7gR4X93vOAB8zs9WBL8pdzLqfw31xgEV5XeLmEa/ivBW4ys8ZmFgv0Bd4LuCbxSeEFz/8DtjnnpgZdj9/MrJaZXVu4XA34IbA92Kr845x72jnXwDnXCO9v/2/Ouf4Bl+UbM0soHOiAmSUAnYFyj/wLy/B3zp0BhgHL8C72veWc2xJsVf4xszeB1UBTM9trZo8GXZPP7gEG4J3xbSx8dQu6KB/VBT4xs014J0IfOucibrhjBKsDrDKzDGAN8L5z7v/Lu5OwHOopIiKVE5Zn/iIiUjkKfxGRCKTwFxGJQAp/EZEIpPAXEYlACn8RkQik8BcRiUAKfxGRCPQfRN1QxGCYg3UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f2d7bfb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lw = 2\n",
    "plt.scatter(X, y,s=10)\n",
    "plt.plot(X, svrs[0].predict(X), color='green', lw=lw, label='linear model')\n",
    "plt.plot(X, svrs[1].predict(X), color='red', lw=lw, label='poly model')\n",
    "plt.plot(X, svrs[2].predict(X), color='navy', lw=lw, label='rbf model')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Custom Kernel\n",
    "* Apart from inbuilt kernals for transforming data, we can write our own custom kernels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_kernel(X,Y):\n",
    "    return np.dot(X, Y.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_circles(n_samples=1000, noise=.05, factor=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel=my_kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel=<function my_kernel at 0x0000019F2F5FED08>, max_iter=-1,\n",
       "  probability=False, random_state=None, shrinking=True, tol=0.001,\n",
       "  verbose=False)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 150)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel=<function my_kernel at 0x0000019F2F5FEC80>, max_iter=-1,\n",
       "  probability=False, random_state=None, shrinking=True, tol=0.001,\n",
       "  verbose=False)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "X = iris.data[:, :2]  # we only take the first two features. We could\n",
    "                      # avoid this ugly slicing by using a two-dim dataset\n",
    "Y = iris.target\n",
    "\n",
    "\n",
    "def my_kernel(X, Y):\n",
    "    \"\"\"\n",
    "    We create a custom kernel:\n",
    "\n",
    "                 (2  0)\n",
    "    k(X, Y) = X  (    ) Y.T\n",
    "                 (0  1)\n",
    "    \"\"\"\n",
    "    M = np.array([[2, 0], [0, 1.0]])\n",
    "    res = np.dot(np.dot(X, M), Y.T)\n",
    "    print (res.shape)\n",
    "    return res\n",
    "\n",
    "\n",
    "h = .02  # step size in the mesh\n",
    "\n",
    "# we create an instance of SVM and fit out data.\n",
    "clf = SVC(kernel=my_kernel)\n",
    "clf.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
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 },
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